Multiband networking weather radar reflectivity factor puzzle fusion method and system

Abstract

The invention relates to the technical field of weather radar networking, in particular to a multiband networking weather radar reflectivity factor puzzle fusion method and system, and the method comprises the following steps: extracting an observation overlapping region of an S-band weather radar network and an X-band weather radar network, and generating mask data containing geographic information; on the basis of the mask data, generating puzzle data on an equal-altitude surface with different S-band weather radar reflectivity factors; based on the mask data, generating jigsaw data on an equal-altitude surface with different X-band weather radar reflectivity factors; and based on a fusion algorithm, fusing the reflectivity factor puzzle data of the S wave band and the X wave band. According to the invention, the networking observation data of the S-band and X-band weather radars are subjected to jigsaw and fusion processing to be processed into a new data product, the collaborative observation result of the S-band and X-band networking weather radars and the low-altitude blind compensation effect can be visually presented, and a data product support is provided for a meteorological department to monitor and early warn weather with strong disastrous conditions.

Description

Technical Field

[0001] This invention relates to a method and system for fusion of reflectivity factor mosaics in a multi-band networked weather radar, belonging to the field of weather radar networking technology. Background Technology

[0002] Weather radar, as an active remote sensing device, is one of the most effective tools for detecting, warning, and assessing heavy rain, severe convective weather (tornadoes, thunderstorms, strong winds, short-duration heavy precipitation, hail), and snow disasters. In addition to measuring the intensity information of meteorological targets, it can also acquire radar radial velocity and polarization information. With the support of algorithms, it can carry out radar quantitative precipitation estimation, severe convective weather feature identification (small-scale vortex, convergence and divergence features, polarization features), and phase identification, effectively supporting meteorological disaster prevention and mitigation work. In recent years, by constructing X-band weather radar systems, we have filled the gaps and gaps in the monitoring of the new generation of weather radar networks and realized a multi-band networked weather radar observation business pattern. However, to make full use of multi-band networked weather radar data, to give full play to the power of multi-band weather radar networks in capturing severe weather at small and medium scales, and to serve the forecasting and early warning of severe weather at small and medium scales, conducting in-depth processing of multi-band networked weather radar observation data is a crucial intermediate link. Current operations still rely on S-band weather radar observation data for forecasting and early warning. Forecasters and other users are relatively unfamiliar with X-band weather radar observation data, and the application of multi-band networked weather radar collaborative observation data is still in its early stages. Under these circumstances, the positive benefits of multi-band networked weather radar in monitoring and early warning of severe weather at small and medium scales are greatly limited, especially in terms of low-altitude blind spot detection benefits. Therefore, exploring and carrying out multi-band networked weather radar data (reflectivity factor) mosaicking and fusion work, forming a mosaicking and fusion method and system that can be used for operational reference, and releasing the dividends of meteorological data are urgent technical problems that need to be solved. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for fusion of reflectivity factor mosaics from multi-band networked weather radars. This method involves deep processing of observation data from multi-band networked weather radars to generate, in image form, mosaic products of different contours of reflectivity factor from S-band weather radars, mosaic products of different contours of reflectivity factor from X-band weather radars, fusion products of mosaic products of different contours of reflectivity factor from multi-band networked weather radars, and their numerical distribution histograms. To provide product support for meteorological departments to monitor and warn of severe weather, to practice the concept of "observation as a service", and to give full play to the "pillar" role of weather radar in the high-quality development of meteorological undertakings.

[0004] To achieve the above objectives, the present invention provides a first aspect of the technical solution: a multi-band networked weather radar reflectivity factor mosaic fusion method, comprising: extracting the overlapping observation area of ​​S-band and X-band weather radar networks to generate mask data containing geographic information; generating contour mosaic data of different reflectivity factors of S-band weather radar based on the mask data; generating contour mosaic data of different reflectivity factors of X-band weather radar based on the mask data; and generating fused data of multi-band networked weather radar contour mosaics of different reflectivity factors based on a fusion algorithm, and generating a product in an image format. (Flowchart shown) Figure 1 .

[0005] Preferably, the process involves extracting the overlapping area of ​​S-band and X-band weather radar network observations to generate mask data containing geographic information. The steps are as follows: Since the X-band weather radar network is nested within the S-band weather radar network, generating the mask data only requires processing the X-band weather radar network. Assume there are 5 X-band weather radars within the network, see... Figure 3 The radar station locations are set at latitude and longitude as (Lon1, Lat1), (Lon2, Lat2), (Lon3, Lat3), (Lon4, Lat4), and (Lon5, Lat5). The maximum detection range of the X-band weather radar is R. max P(LonP, LatP) can be any point in space, within the coverage area of ​​the S-band weather radar network. The distance R from point P to each X-band weather radar station is calculated using the distance formula (1) between two points. x By determining R x With R max The relationship between them determines the observation overlap area; When R x ≤R max If the observations are true, then point P is determined to be within the observation overlap area; otherwise, point P is determined to be outside the observation overlap area. When only point P is within a distance R of one X-band weather radar. x Equal to R max The remaining four X-band weather radars are located at a distance of R. x Greater than R max At that time, the spatial position where point P sits is the boundary of the observation overlap area; following this method, the coverage area of ​​the S-band weather radar network is traversed to obtain the observation overlap area, and the boundary points of the observation overlap area are assigned a value of 1, and the points within the observation overlap area are assigned a value of 0, while retaining the latitude and longitude geographic information, to generate mask data containing geographic information.

[0006] (1) Preferably, the step involves generating contour mosaic data with different reflectivity factors for S-band weather radar based on mask data. The steps are as follows: First, determine the height (Height1) of the contour surface to be pieced together, setting Height1 to (100m, 250m, 500m, 750m, 1000m, 2000m, 3000m). Then, iterate through the mask data space coverage area of ​​point P and calculate the distance R from point P to the S-band weather radar station. s ,( Figure 3 ), the elevation of the contour surface Heigh1, the elevation of the radar station Heigh2, and the slant distance R from point P to the radar station. s The elevation angle θ from the radar station to point P is calculated using trigonometric functions. The elevation angle θ and slant range R are then used to calculate the elevation angle. s Reverse lookup of R near the elevation angle θ (θ±1°) of S-band weather radar s Nearby distance to the library (R) s If there is corresponding detection data (in the database / 1000±1), the reflectivity factor value (Value) is obtained using bilinear interpolation. If no data is found, a value of -999.0 is assigned. Following this method, the reflectivity factor values ​​(Value1, Value2, Value3) for each S-band weather radar at point P are obtained. Value1, Value2, and Value3 are compared, and the maximum value is taken as the reflectivity factor value at point P. After traversing the mask data spatial coverage area at point P, the reflectivity factor mosaic data of the S-band weather radar at height Height1 is obtained. Finally, different values ​​are assigned to Height1, and the above steps are repeated to finally obtain the reflectivity factor mosaic data of the S-band weather radar at heights of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m.

[0007] Preferably, spatiotemporal preprocessing is required before mosaicking the data from multiple S-band weather radars. The steps are as follows: First, the observation time information is obtained by reading the radar base data file name. For example, the field "20251229164908" indicates that the observation time was 16:27:02 on December 29, 2025. Radar base data that are close in time and suitable for mosaicking are matched based on the observation time difference between radars being less than 3 minutes. Second, according to the "Standard Format of Weather Radar Base Data (Version V1.0)," the physical parameters of reflectivity factors are decoded from the S-band networked weather radar base data, and the values ​​from four distance libraries are averaged to obtain vector data with radial direction as the basic storage unit and a spatial resolution (library length) of 1000m, which is used for S-band networked weather radar reflectivity factor mosaicking.

[0008] Preferably, in the reflectivity factor mosaic of the S-band networked weather radar, point P (elevation angle θ and slant range R) s The value at ) The bilinear interpolation method employed is as follows: First, it is assumed that the R value near the elevation angle θ (θ±1°) of the S-band weather radar at point P is calculated. s Nearby distance to the library (R) s The corresponding detection data ( / 1000±1 library) are as follows: , , , Corresponding to (elevation angle θ-1, R) s / 1000-1 (elevation angle θ-1, R) s / 1000+1 (elevation angle θ+1, R) s / 1000-1 (elevation angle θ+1, R) s The reflectivity factor of the S-band weather radar at / 1000+1 (database) is then calculated using linear interpolation according to Formula 2. , Then, the second linear interpolation is used to calculate... .

[0009] (2) Preferably, the step of generating contour patch data with different reflectivity factors for X-band weather radar based on mask data involves the following steps: First, determine the height (Height1) of the contour surface to be patched, setting Height1 to (100m, 250m, 500m, 750m, 1000m, 2000m, 3000m). Then, iterate through the spatial coverage area of ​​the mask data for point P and calculate the distance R from point P to the X-band weather radar station. x ( Figure 3 ), the elevation of the contour surface Heigh1, the elevation of the radar station Heigh2, and the slant distance R from point P to the radar station. x The elevation angle θ from the radar station to point P is calculated using trigonometric functions. The elevation angle θ and slant range R are then used to calculate the elevation angle. x Reverse lookup of R near the elevation angle θ (θ±1°) of S-band weather radar x Nearby distance to the library (R) xIf there is corresponding detection data (in the database / 1000±1), the reflectivity factor value (Value) is obtained using bilinear interpolation. If no data is found, a value of -999.0 is assigned. Following this method, the reflectivity factor values ​​(Value1, Value2, Value3) for each X-band weather radar at point P are obtained. Value1, Value2, and Value3 are compared, and the maximum value is taken as the reflectivity factor value at point P. After traversing the mask data spatial coverage area at point P, the X-band weather radar reflectivity factor mosaic data at height 1 is obtained. Finally, different values ​​are assigned to Height1, and the above steps are repeated to finally obtain S-band weather radar reflectivity factor mosaic data at heights of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m. Furthermore, the time-dimensional matching processing before mosaicking multiple X-band weather radar data is the same as the preprocessing before mosaicking S-band weather radar data. The spatial preprocessing, according to the patent "S and X-band Networked Weather Radar Reflectivity Factor Interactive Verification Method and System," reduces the data spatial resolution from 60m to 250m, and then averages the values ​​from the four range databases to obtain data with a spatial resolution of 1000m. The dual-line interpolation method is consistent with the S-band weather radar data interpolation method; only the R... s Replace with R x For details, please refer to the relevant statements above.

[0010] Preferably, the step of generating multi-band networked weather radar reflectivity factor mosaic data at different elevation levels based on a fusion algorithm is as follows: First, using S-band and X-band weather radar reflectivity factor mosaic data at altitudes of 250m, 500m, and 750m as datasets, the S-band and X-band weather radar reflectivity factor mosaic data are matched one-to-one by using time differences of less than 3 minutes and equal elevation levels (e.g., 250m) to form three data pairs. Then, the fusion algorithm is used to generate multi-band weather radar reflectivity factor mosaic fusion data at altitudes of 250m, 500m, and 750m.

[0011] Preferably, the fusion algorithm used in the multi-band networked weather radar reflectivity factor mosaic fusion processing at different elevations is as follows: First, the mask data spatial coverage area is traversed from point P. Second, using the location information of point P, the values ​​of the S-band and X-band weather radar reflectivity factor mosaic data at point P are retrieved to obtain the two-band weather radar reflectivity factor value pairs (Value1, Value2) at point P. Finally, when Value1 = -999.0, the mosaic fusion value Value at point P = Value2. When Value2 = -999.0, the mosaic fusion value Value at point P = Value1. When Value1 ≠ -999.0 and Value2 ≠ -999.0, Value1 and Value2 are compared, and the maximum value is taken as the fused mosaic value at point P. After traversing the mask data spatial coverage area from point P, the multi-band weather radar reflectivity factor mosaic fusion data at elevation 1 is obtained.

[0012] Preferably, the image generation of multi-band networked weather radar reflectivity factor mosaics and fusion products at different elevations involves the following steps: First, the S-band and X-band weather radar reflectivity factor mosaic data at altitudes of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m, and the multi-band networked weather radar reflectivity factor mosaic data at altitudes of 250m, 500m, and 750m are classified and color-coded. The classification interval is 5 dBZ, and the classification levels are ≤-5 dBZ, ≤0 dBZ, ≤5 dBZ, ≤10 dBZ, ≤15 dBZ, ≤20 dBZ, ≤25 dBZ, ≤30 dBZ, ≤35 dBZ, ≤40 dBZ, ≤45 dBZ, ≤50 dBZ, ≤55 dBZ, ≤60 dBZ, ≤65 dBZ, and >65 dBZ, for a total of 16 levels. Next, using the Visual C++ development platform, different levels are assigned colors to generate multi-band networked weather radar reflectivity factor contour mosaics and fused products in a pseudo-color image format. The numerical distribution of the multi-band weather radar reflectivity factor contour mosaics and fused products is then statistically analyzed at different levels and presented as a bar chart. See the product series diagram below. Figure 4-10 .

[0013] To achieve the above objectives, the present invention provides a second aspect of the technical solution: a multi-band networked weather radar reflectivity factor mosaic fusion system, comprising: a radar data matching and processing module; a mask data generation module; a radar mosaic fusion data generation module; and a radar mosaic fusion product generation module. The mosaic fusion system process is described below. Figure 2 .

[0014] Preferably, the radar data matching and processing module, under the Visual C++ 6.0 software platform, automatically matches the radar base data observed by the S-band weather radar network and the X-band weather radar network based on filename information and with a time constraint of less than 3 minutes. It then decodes the base data to generate reflectivity factor volume scan data. Finally, it performs spatial processing on the S-band and X-band weather radar reflectivity factors to obtain an S-band and X-band weather radar reflectivity factor dataset with a spatial resolution of 1000m. Preferably, the mask data generation module, using the Visual C++ 6.0 software platform, acquires metadata information of each radar in the X-band weather radar network. Based on the location relationship, site information, and maximum detection range of the S-band and X-band weather radar networks, it extracts the observation overlap area and generates mask data containing geographical information. Preferably, the radar mosaic fusion data generation module, using the Visual C++ 6.0 software platform, based on mask data and utilizing the S-band weather radar reflectivity factor dataset, obtains S-band weather radar reflectivity factor mosaic data at altitudes of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m through interpolation and numerical comparison. Based on mask data and utilizing the X-band weather radar reflectivity factor dataset, obtains X-band weather radar reflectivity factor mosaic data at altitudes of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m through interpolation and numerical comparison. Using mosaic data of different contour surfaces of S-band and X-band weather radar reflectivity factors, multi-band networked weather radar reflectivity factor mosaic fusion data at altitudes of 250m, 500m, and 750m through one-to-one matching and data fusion.

[0015] Preferably, the radar mosaic fusion product generation module, using the Visual C++ 6.0 software platform, classifies and colors the mosaic data of different contours of S-band weather radar reflectivity factors, X-band weather radar reflectivity factors, and multi-band networked weather radar reflectivity factors. It then generates S-band, X-band, and multi-band networked weather radar reflectivity factors mosaic fusion products in the form of pseudo-color images. Furthermore, it provides hierarchical statistics, displaying the distribution of multi-band weather radar reflectivity factors, different contours, and fused values ​​in a bar chart.

[0016] The present invention has at least the following beneficial effects: 1. This invention explores the mosaicking and fusion of multi-band networked weather radar data (reflectivity factor), forming a mosaicking and fusion method and system that can be used for operational reference. The method and system are used for the in-depth processing of high-density multi-band networked weather radar data (for monitoring and early warning of highly disastrous weather) built by meteorological departments, releasing the dividends of meteorological data elements and serving meteorological disaster prevention and mitigation work. 2. This invention focuses on the data mosaicking and fusion of reflectivity factors from multi-band networked weather radars. Using S- and X-band networked weather radar base data as the data source, it performs in-depth data processing procedures such as spatiotemporal matching and processing, mask data (observation overlap area) generation, radar data mosaicking and fusion, etc. Further processing, including grading and color marking of the mosaicked and fused data, and grading statistics, visualizes the data at 100m, 250m, 500m, 750m, 1000m, 2000m, etc. The product includes S-band weather radar reflectivity factor mosaic at 3000m altitude, X-band weather radar reflectivity factor mosaic at 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m altitudes, and multi-band networked weather radar reflectivity factor mosaic fusion at 250m, 500m, and 750m altitudes. It simultaneously outputs multi-band weather radar reflectivity factor mosaics at different elevations and fused numerical distribution maps in the form of bar charts. Breaking away from the fragmented operational model of single-unit, single-band weather radar networks supporting severe convective weather forecasting and early warning, this initiative provides forecasters and other users with rich, intuitive, and collaborative new radar data products through multi-unit, multi-band networked weather radar data mosaicking and fusion processing. 3. This invention, developed using the Visual C++ 6.0 platform, technically implements a multi-band networked weather radar reflectivity factor mosaic fusion system; The methods and systems can be directly applied in operational settings at frontline stations, filling operational gaps and enhancing stations' ability to assess severe weather and improve their level of meteorological modernization. Attached Figure Description

[0017] Figure 1 This is a flowchart of the multi-band networked weather radar reflectivity factor mosaic fusion method of the present invention; Figure 2 This is a flowchart of the multi-band networked weather radar reflectivity factor mosaic fusion system of the present invention; Figure 3 This is a schematic diagram illustrating the extraction of the overlapping area (mask) in the multi-band networked weather radar observations of the present invention; Figure 4 This is an example diagram showing the reflectivity factor mosaic product and numerical distribution of S-band and X-band weather radar at an altitude of 3000m according to the present invention. Figure 5This is an example diagram showing the reflectivity factor mosaic product and numerical distribution of S-band and X-band weather radar at an altitude of 2000m according to the present invention. Figure 6 This is an example diagram showing the reflectivity factor mosaic product and numerical distribution of S-band and X-band weather radar at an altitude of 1000m according to the present invention. Figure 7 This is an example diagram showing the reflectivity factor mosaic product and numerical distribution of the S-band and X-band weather radar at an altitude of 750m according to the present invention. Figure 8 This is an example diagram showing the reflectivity factor mosaic product and numerical distribution of S-band and X-band weather radar at a height of 500m according to the present invention. Figure 9 This is an example diagram showing the reflectivity factor mosaic product and numerical distribution of S-band and X-band weather radar at a height of 250m according to the present invention; Figure 10 This is an example diagram showing the reflectivity factor mosaic product and numerical distribution of S-band and X-band weather radar at a height of 100m according to the present invention. Figure 11 This is an example diagram showing the reflectivity factor mosaic fusion product and numerical distribution of the multi-band networked weather radar at altitudes of 750m, 500m, and 250m according to the present invention. Detailed Implementation

[0018] The following will describe in detail the implementation of this application with reference to the accompanying drawings and embodiments, so that the implementation process of how this application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0019] like Figures 1-5 As shown, a multi-band networked weather radar reflectivity factor mosaic fusion method is proposed. Figure 1 The process includes: S1, extracting overlapping areas from S-band and X-band weather radar networks to generate mask data containing geographic information; S2, generating contour mosaic data with different reflectivity factors for S-band weather radar based on the mask data; S3, generating contour mosaic data with different reflectivity factors for X-band weather radar based on the mask data; and S4, generating fused data of contour mosaics with different reflectivity factors for multi-band networked weather radar based on a fusion algorithm, and generating an image-based product. A multi-band networked weather radar reflectivity factor mosaic fusion system ( Figure 2 The system includes: a radar data matching and processing module; a mask data generation module; a radar mosaic fusion data generation module; and a radar mosaic fusion product generation module. The radar data matching and processing module is used to construct a dataset of reflectivity factors for S-band and X-band weather radars that are temporally close (less than 3 minutes) and have a consistent spatial resolution (1000m). The mask data generation module is used to generate the geographically information-containing mask data required for mosaic fusion data and products. Figure 3The radar mosaic fusion data generation module is used to generate mosaic and fusion data of different contour surfaces for S-band and X-band networked weather radar reflectivity factors required for mosaic and fusion products. The radar mosaic fusion product generation module is used to generate S-band weather radar reflectivity factor mosaic products at altitudes of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m; X-band weather radar reflectivity factor mosaic products at altitudes of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m; multi-band networked weather radar reflectivity factor mosaic fusion products at altitudes of 250m, 500m, and 750m; and a bar chart of the numerical distribution of the mosaic and fusion products. Figure 4-11 ).

[0020] When using it, you need to create eight folders (two groups) in the system directory: “Data / S1”, “Data / S2”, “Data / S3”, and “Data / X1”, “Data / X2”, “Data / X3”, “Data / X4”, and “Data / X5”. These folders will store the base data from three S-band weather radars and five X-band weather radars, respectively. Figure 2 The process involves the background processing of radar data through modules for radar data matching and processing, mask data generation, radar mosaic fusion data generation, and radar mosaic fusion product generation. The resulting image output includes S-band weather radar reflectivity factor mosaic products at altitudes of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m, along with their numerical distribution histograms; and X-band weather radar reflectivity factor mosaic products at altitudes of 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m, along with their numerical distribution histograms. Figure 4-10 ), multi-band networked weather radar reflectivity factor mosaic fusion product images at altitudes of 250m, 500m, and 750m, and their numerical distribution bar charts ( Figure 11 ).

[0021] In summary, the proposed multi-band networked weather radar reflectivity factor mosaicking and fusion method and system, based on S- and X-band networked weather radar volume scan data, through spatiotemporal matching and processing of observation data, mask extraction, technical mosaicking, data fusion, classification and labeling, provides mosaicking and fusion product images of different contour surfaces of multi-band networked weather radar reflectivity factors. Figure 4-11 Developed on the Visal C++ 6.0 platform, the system provides weather stations with an intuitive, visual, and easy-to-use operational interface, serving as a practical weather analysis tool. This invention not only fills a gap in operational capabilities but also provides system platform and data product support for enhancing weather forecasting and early warning capabilities at weather stations and improving the level of modern meteorological operations.

[0022] The foregoing description illustrates and describes several preferred embodiments of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for mosaicking and fusing reflectivity factors from a multi-band networked weather radar, characterized in that, Includes the following steps: Extract the observation overlap area between the S-band weather radar network and the X-band weather radar network to generate mask data containing geographic information; Based on the mask data, mosaic data of different contour surfaces with reflectivity factors of S-band weather radar is generated. Based on the mask data, mosaic data of different contour surfaces with reflectivity factors of X-band weather radar is generated; Based on the fusion algorithm, the reflectivity factor mosaic data of S-band and X-band are fused to generate mosaic fusion data of reflectivity factor of multi-band networked weather radar on different contour surfaces. Multi-band fused data is visualized to generate products.

2. The method for mosaicking and fusion of reflectivity factors in a multi-band networked weather radar according to claim 1, characterized in that: The mask data containing geographic information is generated by extracting the overlapping area of ​​S-band weather radar network and X-band weather radar observation based on the basic information of the location and detection range of multi-band networked weather radar. The mosaic data on different contour surfaces of S-band and X-band weather radar reflectivity factors are generated by setting the height, traversing the mask, calculating the elevation angle, azimuth, range information of each grid point relative to each radar in the S-band and X-band weather radar network, as well as the corresponding reflectivity factor value, and taking the maximum value. The fusion data of different contour surface mosaics is obtained by fusing the reflectivity factor mosaic data of S and X band weather radars at different altitudes, and then assigning them to hierarchical thresholds and colors.

3. The multi-band networked weather radar reflectivity factor mosaic fusion method according to claim 2, characterized in that: In the process of mosaicking and fusion processing of reflectivity factors of S-band and X-band networked weather radars, there are constraints on the observation time difference between weather radar data of different bands, the spatial resolution of mosaic fusion products and the height of contour surfaces. The constraints are: time difference constraint, spatial resolution constraint, S-band weather radar constraint, X-band weather radar constraint, and mosaic product height constraint; The time difference constraint is that time information is obtained through the file names of S-band and X-band weather radar base data, and the time difference is less than 3 minutes. The spatial resolution constraint is to uniformly process the reflectivity factor distance resolution of S-band and X-band weather radar to 1000m, which meets the resolution constraint of 1000m required for the mosaic product. The S-band weather radar is constrained by setting the reflectivity factor data resolution to 250m and averaging the values ​​from four range databases. The X-band weather radar is constrained by a fixed reflectivity factor range resolution of 60m, and the spatial resolution is processed to 250m. The values ​​from the four range databases are then averaged. The height constraints for jigsaw puzzle products are as follows: the height of the contour surface of the jigsaw puzzle products is 100m, 250m, 500m, 750m, 1000m, 2000m, and 3000m; the height of the contour surface of the jigsaw puzzle fusion products is 250m, 500m, and 750m.

4. The method for mosaicking and fusion of reflectivity factors in a multi-band networked weather radar according to claim 3, characterized in that: The method for generating the mask data containing geographic information is as follows: Based on the latitude and longitude of the S-band and X-band radar station sites and the maximum detection range R of the X-band... max Iterate through any spatial point P within the S-band coverage area and calculate the distance R from point P to each X-band radar. x ; When R x ≤R max At that time, it was determined that point P was within the observation range of the X-band; This determines the observation overlap area, assigns a value of 1 to the boundary points of the overlap area and a value of 0 to the points within the overlap area, while retaining latitude and longitude information to form rasterized mask data.

5. The multi-band networked weather radar reflectivity factor mosaic fusion method according to claim 4, characterized in that: The method for generating the S-band contour map data is as follows: Within the mask coverage area, for each grid point P, calculate the slant range R between point P and each S-band radar station. s And the elevation angle θ calculated from this; Search for detection records near the elevation angle θ and the distance from the library number in the radar observation data. If they exist, obtain the reflectivity factor value Value of the radar at point P by bilinear interpolation. If they do not exist, mark the radar as missing. The maximum reflectivity factor value obtained from each radar is taken as the S-band reflectivity factor mosaic value at point P; repeated traversal can obtain S-band mosaic data under a specified contour surface.

6. The method for mosaicking and fusion of reflectivity factors in a multi-band networked weather radar according to claim 5, characterized in that: The method for generating the X-band contour map data is as follows: Within the mask coverage area, calculate the slant range R between point P and each X-band radar station. x And the elevation angle θ calculated from this; Search for detection records near the elevation angle θ and the distance from the library number in the radar observation data. If they exist, obtain the reflectivity factor value Value of the radar at point P by bilinear interpolation. If they do not exist, mark the radar as missing. The maximum reflectivity factor value obtained from each radar within the network is taken as the X-band reflectivity factor mosaic value at point P; repeated traversal can obtain X-band mosaic data under a specified contour surface.

7. The multi-band networked weather radar reflectivity factor mosaic fusion method according to claim 6, characterized in that: The fusion algorithm is implemented by using S-band and X-band mosaic data generated on elevation surfaces of 250m, 500m and 750m as the data source; The S and X band mosaic data were paired to form data pairs based on the principle that the time difference was less than 3 minutes and the contour surfaces were of the same height. For each grid point P within the mask, take the value (Value1, Value2) of the paired data pair at point P; When Value1 is a missing test flag, Value2 is used as the merged value; when Value2 is a missing test flag, Value1 is used as the merged value. When both Value1 and Value2 are valid values, the larger of the two values ​​is used as the merged mosaic value at point P. Repeat this process for all grid points to obtain multi-band mosaic fusion data at the heights of 250m, 500m, and 750m.

8. The method for mosaicking and fusion of reflectivity factors in a multi-band networked weather radar according to claim 7, characterized in that: The image generation method for the puzzle and fusion products is as follows: the raster reflectance factor value is divided into 16 levels with an interval of 5dBZ; each level is assigned a corresponding false color; the levels are mapped to false color images based on the Visual C++ development platform; and the raster values ​​are statistically graded and the numerical distribution is represented in the form of a bar chart for visualization analysis.

9. The multi-band networked weather radar reflectivity factor mosaic fusion method according to claim 8, characterized in that: The mask data, S-band and X-band contour mosaic data, and multi-band mosaic fusion data and their image products are all stored in raster data form and described in the Earth coordinate system by longitude, latitude and corresponding values ​​or colors.

10. A multi-band networked weather radar reflectivity factor mosaic fusion system according to any one of claims 1-9, characterized in that: The system includes: The radar data matching and processing module is used to perform time matching, decoding, and spatial resolution standardization on the base data of S-band and X-band weather radars to generate a standardized reflectivity factor dataset. The mask data generation module is used to extract the observation overlap area and generate mask data containing geographic information based on the site and detection range information of the S-band and X-band weather radar networks. The radar mosaic fusion data generation module is connected to the radar data matching and processing module and the mask data generation module, respectively. It is used to generate S-band and X-band reflectivity factor mosaic data with specified contour heights based on the mask data and standardized dataset, and generate multi-band mosaic fusion data according to the fusion algorithm. The radar mosaic fusion product generation module is connected to the radar mosaic fusion data generation module. It is used to perform hierarchical coloring and graphical rendering on the mosaic data and fusion data to generate false-color image products and numerical distribution statistical charts.

Images